A marketing company collects clickstream data The company sends the data to Amazon Kinesis Data Firehose and stores the data in Amazon S3 The company wants to build a series of dashboards that will be used by hundreds of users across different departments The company will use Amazon QuickSight to develop these dashboards The company has limited resources and wants a solution that could scale and provide daily updates about clickstream activity Which combination of options will provide the MOST cost-effective solution? (Select TWO )

• A.Use Amazon Redshift to store and query the clickstream data
• B.Use S3 analytics to query the clickstream data
• C.Use QuickSight with a direct SQL query
• D.Use the QuickSight SPICE engine with a daily refresh
• E.Use Amazon Athena to query the clickstream data in Amazon S3

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An online retail company with millions of users around the globe wants to improve its ecommerce analytics capabilities. Currently, clickstream data is uploaded directly to Amazon S3 as compressed files. Several times each day, an application running on Amazon EC2 processes the data and makes search options and reports available for visualization by editors and marketers. The company wants to make website clicks and aggregated data available to editors and marketers in minutes to enable them to connect with users more effectively.
Which options will help meet these requirements in the MOST efficient way? (Choose two.)

• A.Use Amazon Kinesis Data Firehose to upload compressed and batched clickstream records to Amazon Elasticsearch Service.
• B.Upload clickstream records to Amazon S3 as compressed files. Then use AWS Lambda to send data to Amazon Elasticsearch Service from Amazon S3.
• C.Use Kibana to aggregate, filter, and visualize the data stored in Amazon Elasticsearch Service. Refresh content performance dashboards in near-real time.
• D.Use Amazon Elasticsearch Service deployed on Amazon EC2 to aggregate, filter, and process the data. Refresh content performance dashboards in near-real time.
• E.Upload clickstream records from Amazon S3 to Amazon Kinesis Data Streams and use a Kinesis Data Streams consumer to send records to Amazon Elasticsearch Service.

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A company has collected more than 100 TB of log files in the last 24 months. The files are stored as raw text in a dedicated Amazon S3 bucket. Each object has a key of the form year-month-day_log_HHmmss.txt where HHmmss represents the time the log file was initially created. A table was created in Amazon Athena that points to the S3 bucket. One-time queries are run against a subset of columns in the table several times an hour.
A data analyst must make changes to reduce the cost of running these queries. Management wants a solution with minimal maintenance overhead.
Which combination of steps should the data analyst take to meet these requirements? (Choose three.)

• A.Convert the log files to Apace Avro format.
• B.Add a key prefix of the form date=year-month-day/ to the S3 objects to partition the data.
• C.Convert the log files to Apache Parquet format.
• D.Add a key prefix of the form year-month-day/ to the S3 objects to partition the data.
• E.Drop and recreate the table with the PARTITIONED BY clause. Run the ALTER TABLE ADD PARTITION statement.
• F.Drop and recreate the table with the PARTITIONED BY clause. Run the MSCK REPAIR TABLE statement.

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A company that monitors weather conditions from remote construction sites is setting up a solution to collect temperature data from the following two weather stations.
* Station A, which has 10 sensors
* Station B, which has five sensors
These weather stations were placed by onsite subject-matter experts.
Each sensor has a unique ID. The data collected from each sensor will be collected using Amazon Kinesis Data Streams.
Based on the total incoming and outgoing data throughput, a single Amazon Kinesis data stream with two shards is created. Two partition keys are created based on the station names. During testing, there is a bottleneck on data coming from Station A, but not from Station B.
Upon review, it is confirmed that the total stream throughput is still less than the allocated Kinesis Data Streams throughput.
How can this bottleneck be resolved without increasing the overall cost and complexity of the solution, while retaining the data collection quality requirements?

• A.Create a separate Kinesis data stream for Station A with two shards, and stream Station A sensor data to the new stream.
• B.Modify the partition key to use the sensor ID instead of the station name.
• C.Reduce the number of sensors in Station A from 10 to 5 sensors.
• D.Increase the number of shards in Kinesis Data Streams to increase the level of parallelism.

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A media company wants to perform machine learning and analytics on the data residing in its Amazon S3 data lake. There are two data transformation requirements that will enable the consumers within the company to create reports:
Daily transformations of 300 GB of data with different file formats landing in Amazon S3 at a scheduled time.
One-time transformations of terabytes of archived data residing in the S3 data lake.
Which combination of solutions cost-effectively meets the company's requirements for transforming the data? (Choose three.)

• A.For archived data, use Amazon SageMaker to perform data transformations.
• B.For archived data, use Amazon EMR to perform data transformations.
• C.For daily incoming data, use Amazon Redshift to perform transformations.
• D.For daily incoming data, use Amazon Athena to scan and identify the schema.
• E.For daily incoming data, use AWS Glue crawlers to scan and identify the schema.
• F.For daily incoming data, use AWS Glue workflows with AWS Glue jobs to perform transformations.

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